Stabilizing the visual system is a crucial issue for any sighted mobile creature, whether it is natural or artificial. The more immune the gaze of an animal or a robot is to various kinds of disturbances, e.g., those created by body or head movements when walking or flying, the less troublesome it will be for the visual system to carry out its many information processing tasks. This arises as typically the visual system, i.e. the human brain or microprocessor in robots, is too slow to process information if the images are slipping across the optical receptor, i.e. the human retina or charge coupled device (CCD) in robots, too rapidly—for the human brain this limit is as low as a few degrees per second, see for example G. Westheimer et al in “Visual acuity in the presence of retinal-image motion” (J Opt. Soc. Am., Vol. 65(7), pp 847-50).
Thus, while viewing a spatially-fixed target, the visual system must compensate for the motion of the head by turning the eyes in a direction opposite to the head. Another complication for vision in frontal-eyed animals is the development of a small area of the retina with a very high visual acuity, the fovea, which covers about 2 degrees of visual angle on average in humans. To get a clear view of the world, the visual control system must turn the eyes so that the image of the object is positioned reproducibly on the same region of the optical sensor whilst the determination of what is being imaged is made. For humans this means keeping the image centered on the fovea which is functionally similar to preventing image motion on a CCD sensor. Having two “eyes” is an added complication for both humans and robots, because the visual system must point both of them accurately enough that the object of regard falls on corresponding points of the two “sensors”; otherwise, double vision would occur. The movements of different body parts are controlled by striated muscles acting around joints. The movements of the eye are no exception, but they have special advantages not shared by skeletal muscles and joints, and so are considerably different.
Two reflexes within the human visual system are the optokinetic reflex, which allows the eye to follow objects in motion when the head remains stationary, and the vestibulo-ocular reflex (VOR) which stabilizes images on the retina during head movement by producing an eye movement in the direction opposite to head movement, thus preserving the image on the center of the visual field. In contrast “smooth pursuit movement” occurs where the eyes follow a moving object. Smooth pursuit tracking is less accurate than the vestibulo-ocular reflex, as it requires the brain to process incoming visual information and supply feedback to turn the eyes and, for a larger range of tracking, the head and/or body. Following an object moving at constant speed is relatively easy, though the eyes will often make saccadic jerks to keep up. The smooth pursuit movement can move the eye at up to 100°/s in adult humans.
With the vestibulo-ocular reflex (VOR) when the head moves to the right, the eyes move to the left, and vice versa and since slight head movement is present all the time, the VOR is very important for stabilizing vision in tasks such as reading. The human VOR system detects head movement and orientation in space without visual input and works even in total darkness or when the eyes are closed. However, in the presence of light, a re-fixation reflex is also added to the movement, where light sources in the periphery can cause the eyes to shift gaze direction under the control of the occipital lobe of the cerebral cortex.
Patients whose VOR is impaired find it difficult to read using print, because they cannot stabilize the eyes during small head tremors. Damage to the VOR system can cause such symptoms as vertigo, dizziness, blurred vision, and imbalance due to incorrect motions of the eyes relative to the head for humans. In robots poor stabilization may result in increased load on microprocessor analysis of the received images as well as imbalance and incorrect determination of movement in observed objects or body parts. Accordingly understanding the VOR in humans is important for:                diagnosis and treatment of patients, see for example R. Vaire “Diagnostic Value of Vestibulo-Ocular Reflex Parameters in the Detection and Characterization of Labyrinthine Lesions” (Otology & Neurotology, Vol. 27(4), pp. 535-541), J. M. Furman et al in “Vestibular Disorders: A Case Study Approach to Diagnosis and Treatment” (Oxford University Press, 2010), J. Edlow et al in “Diagnosis and initial management of cerebellar infarction” (The Lancet—Neurology, Vol. 7(10), pp 951-964), “Using Eye Movements as an Experimental Probe of Brain Function—A Symposium” (Elsevier, 2008);        providing visual prosthesis, often referred to as a bionic eye, for patients and presented for example in J. Loudin in “Optoelectronic retinal prosthesis: system design and performance” (J. Neural Eng., Vol. 4, pp S72-S84), C. Zhou et al in “Implantable Imaging System for Visual Prosthesis” (Artificial Organs, Vol. 34(6), pp 518-522) and L. Turicchia et al in “A Low-Power Imager and Compression Algorithms for a Brain-Machine Visual Prosthesis for the Blind” (Biosensing, Proc. SPIE, Volume 7035, pp. 703510-703510-13);        developing vision systems for robots, see for example S. Viollet et al in “A high speed gaze control system based on the Vestibulo-Ocular Reflex” (Robotics and Autonomous Systems, 50(4), pp 147-161), R. Kaushik et al “Implementation of Bio-Inspired Vestibulo-Ocular Reflex in a Quadrupedal Robot” (IEEE Conf. Robotics and Automation, 2007, pp 4861-4866), and A. Lenz et al in “An adaptive gaze stabilization controller inspired by the vestibulo-ocular reflex” (Bioinspiration and Biomimetics, Vol. 3, Iss. 3, pp. 035001).        
The VOR has been studied mathematically for several decades, leading to many models in the literature, see for example O. J-M. D Coenen et al in “Dynamical Model of Context Dependencies for the Vestibulo-Ocular Reflex” (Advances in Neural Information Processing Systems 8, MIT Press), S. Ramat et al in “Oculomotor responses to active head movements in darkness: formulation and testing of a mathematical model” (Ann N Y Acad Sci. 2001, pp 482-5. 2000) and S. Raphan et al in “The Vestibulo-Ocular Reflex in Three Dimensions” (Exp. Brain Res. Vol. 145, pp 1-27; Dieterich et al. 2003). Whilst these earlier models of the VOR provided insight into vestibular processes there remain many unresolved issues. The most important issue concerns data analysis and the “switched” nature of VOR responses. While VOR trajectories contain segments of fast and slow eye velocity, the prior art disregards the fast phases and links slow phase segments of eye velocity to estimate VOR dynamics.
Assuming correct pre-classification of nystagmus segments with these prior art approaches, then the clinical analysis of the VOR is limited to calculating the gain, time constant, and asymmetry of the envelope of the slow-phase response during step or harmonic rotations. As such this “envelope” approach results in the loss or misinterpretation of information, especially since the fast phases of nystagmus can also carry clinically relevant information. Furthermore, “envelope” based estimates are only valid (unbiased) for a continuous system while the VOR is clearly discontinuous in its response dynamics.
Indeed, the VOR falls into a class of systems called hybrid systems which exhibit multiple response strategies e.g. slow compensation and fast saccadic redirection for the human eye. It has been previously shown within the prior art that treating a hybrid system as a non-hybrid system leads to errors in identified reflex dynamics, which clearly impacts on diagnostic decisions and on interpretations of neural data. More recently improvements have been made to the estimation of VOR dynamics by pooling selected slow phase segments and separating the synergistic effects of fast phase end-points (i.e. the slow-phase initial conditions) from the common head-driven VOR dynamics. This improves the accuracy of estimated parameters, but again requires the correct pre-classification of the data.
Accordingly, an important issue for prior art mathematical analysis techniques is the requirement to classify the nystagmus segments before applying any analysis techniques, wherein this classification is preferably performed non-subjectively. Existing algorithms are based on ad-hoc measures and are not objective, since they require manual corrections especially for non-harmonic stimuli. Therefore it would be beneficial to overcome the above limitations by classification, i.e. segmentation of the data record into fast and slow phase pieces, and possibly artifacts or outliers, and identification concurrently and objectively as well as analyzing multi-input systems. It would be evident that improved analysis of the VOR in humans provides for:
improved diagnosis of patients;
improved treatment of patients;
improvements in performance of visual prosthesis for patients; and
improved performance of vision systems for mobile robotic systems.
Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.